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- ItemOptimized Deep Learning Model as a Basis for Fast UAV Mapping of Weed Species in Winter Wheat Crops(Basel : MDPI AG, 2021) de Camargo, Tibor; Schirrmann, Michael; Landwehr, Niels; Dammer, Karl-Heinz; Pflanz, MichaelWeed maps should be available quickly, reliably, and with high detail to be useful for site-specific management in crop protection and to promote more sustainable agriculture by reducing pesticide use. Here, the optimization of a deep residual convolutional neural network (ResNet-18) for the classification of weed and crop plants in UAV imagery is proposed. The target was to reach sufficient performance on an embedded system by maintaining the same features of the ResNet-18 model as a basis for fast UAV mapping. This would enable online recognition and subsequent mapping of weeds during UAV flying operation. Optimization was achieved mainly by avoiding redundant computations that arise when a classification model is applied on overlapping tiles in a larger input image. The model was trained and tested with imagery obtained from a UAV flight campaign at low altitude over a winter wheat field, and classification was performed on species level with the weed species Matricaria chamomilla L., Papaver rhoeas L., Veronica hederifolia L., and Viola arvensis ssp. arvensis observed in that field. The ResNet-18 model with the optimized image-level prediction pipeline reached a performance of 2.2 frames per second with an NVIDIA Jetson AGX Xavier on the full resolution UAV image, which would amount to about 1.78 ha h−1 area output for continuous field mapping. The overall accuracy for determining crop, soil, and weed species was 94%. There were some limitations in the detection of species unknown to the model. When shifting from 16-bit to 32-bit model precision, no improvement in classification accuracy was observed, but a strong decline in speed performance, especially when a higher number of filters was used in the ResNet-18 model. Future work should be directed towards the integration of the mapping process on UAV platforms, guiding UAVs autonomously for mapping purpose, and ensuring the transferability of the models to other crop fields.
- ItemMonitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery(Basel : MDPI, 2016) Schirrmann, Michael; Giebel, Antje; Gleiniger, Franziska; Pflanz, Michael; Lentschke, Jan; Dammer, Karl-HeinzMonitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned aerial vehicles (UAV) over an 11 ha field. Flight missions were conducted at approximately 50 m in altitude with a commercial copter and camera system—three missions were performed between booting and maturing of the wheat plants and one mission after tillage. Ultra-high resolution orthoimages of 1.2 cm·px−1 and surface models were generated for each mission from the standard red, green and blue (RGB) aerial images. The image variables were extracted from image tone and surface models, e.g., RGB ratios, crop coverage and plant height. During each mission, 20 plots within the wheat canopy with 1 × 1 m2 sample support were selected in the field, and the leaf area index, plant height, fresh and dry biomass and nitrogen concentrations were measured. From the generated UAV imagery, we were able to follow the changes in early senescence at the individual plant level in the wheat crops. Changes in the pattern of the wheat canopy varied drastically from one mission to the next, which supported the need for instantaneous observations, as delivered by UAV imagery. The correlations between the biophysical parameters and image variables were highly significant during each mission, and the regression models calculated with the principal components of the image variables yielded R2 values between 0.70 and 0.97. In contrast, the models of the nitrogen concentrations yielded low R2 values with the best model obtained at flowering (R2 = 0.65). The nitrogen nutrition index was calculated with an accuracy of 0.10 to 0.11 NNI for each mission. For all models, information about the surface models and image tone was important. We conclude that low-cost RGB UAV imagery will strongly aid farmers in observing biophysical characteristics, but it is limited for observing the nitrogen status within wheat crops.
- ItemEffect of Liquid Hot Water Pretreatment on Hydrolysates Composition and Methane Yield of Rice Processing Residue(Basel : MDPI, 2021) López González, Lisbet Mailin; Heiermann, MonikaLignocellulosic rice processing residue was pretreated in liquid hot water (LHW) at three different temperatures (140, 160, and 180 °C) and two pretreatment times (10 and 20 min) in order to assess its effects on hydrolysates composition, matrix structural changes and methane yield. The concentrations of acetic acid, 5-hydroxymethylfurfural and furfural increased with pretreatment severity (log Ro). The maximum methane yield (276 L kg−1 VS) was achieved under pretreatment conditions of 180 °C for 20 min, with a 63% increase compared to untreated biomass. Structural changes resulted in a slight removal of silica on the upper portion of rice husks, visible predominantly at maximum severity. However, the outer epidermis was kept well organized. The results indicate, at severities 2.48 ≤ log Ro ≤ 3.66, a significant potential for the use of LHW to improve methane production from rice processing residue.
- ItemHydrothermal Carbonization: Modeling, Final Properties Design and Applications: A Review(Basel : MDPI, 2018-1-16) Román, Silvia; Libra, Judy; Berge, Nicole; Sabio, Eduardo; Ro, Kyoung; Li, Liang; Ledesma, Beatriz; Álvarez, Andrés; Bae, SunyoungActive research on biomass hydrothermal carbonization (HTC) continues to demonstrate its advantages over other thermochemical processes, in particular the interesting benefits that are associated with carbonaceous solid products, called hydrochar (HC). The areas of applications of HC range from biofuel to doped porous material for adsorption, energy storage, and catalysis. At the same time, intensive research has been aimed at better elucidating the process mechanisms and kinetics, and how the experimental variables (temperature, time, biomass load, feedstock composition, as well as their interactions) affect the distribution between phases and their composition. This review provides an analysis of the state of the art on HTC, mainly with regard to the effect of variables on the process, the associated kinetics, and the characteristics of the solid phase (HC), as well as some of the more studied applications so far. The focus is on research made over the last five years on these topics. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.
- ItemComparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture(Basel : MDPI, 2019) Riebe, Daniel; Erler, Alexander; Brinkmann, Pia; Beitz, Toralf; Löhmannsröben, Hans-Gerd; Gebbers, RobinThe lack of soil data, which are relevant, reliable, affordable, immediately available, and sufficiently detailed, is still a significant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within fields, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, different calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural fields in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The first method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both fields. The second univariate model derived the calibration from the reference analytics of all samples from one field. The prediction is validated by LIBS data of the second field. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the first field are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the first field and independently on the second field data. The second univariate method yielded better calibration and prediction results compared to the first method, since matrix effects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method.
- ItemIoT-Based Sensor Data Fusion for Determining Optimality Degrees of Microclimate Parameters in Commercial Greenhouse Production of Tomato(Basel : MDPI, 2020) Rezvani, Sayed Moin-eddin; Abyaneh, Hamid Zare; Shamshiri, Redmond R.; Balasundram, Siva K.; Dworak, Volker; Goodarzi, Mohsen; Sultan, Muhammad; Mahns, BenjaminOptimum microclimate parameters, including air temperature (T), relative humidity (RH) and vapor pressure deficit (VPD) that are uniformly distributed inside greenhouse crop production systems are essential to prevent yield loss and fruit quality. The objective of this research was to determine the spatial and temporal variations in the microclimate data of a commercial greenhouse with tomato plants located in the mid-west of Iran. For this purpose, wireless sensor data fusion was incorporated with a membership function model called Optimality Degree (OptDeg) for real-time monitoring and dynamic assessment of T, RH and VPD in different light conditions and growth stages of tomato. This approach allows growers to have a simultaneous projection of raw data into a normalized index between 0 and 1. Custom-built hardware and software based on the concept of the Internet-of-Things, including Low-Power Wide-Area Network (LoRaWAN) transmitter nodes, a multi-channel LoRaWAN gateway and a web-based data monitoring dashboard were used for data collection, data processing and monitoring. The experimental approach consisted of the collection of meteorological data from the external environment by means of a weather station and via a grid of 20 wireless sensor nodes distributed in two horizontal planes at two different heights inside the greenhouse. Offline data processing for sensors calibration and model validation was carried in multiple MATLAB Simulink blocks. Preliminary results revealed a significant deviation of the microclimate parameters from optimal growth conditions for tomato cultivation due to the inaccurate timer-based heating and cooling control systems used in the greenhouse. The mean OptDeg of T, RH and VPD were 0.67, 0.94, 0.94 in January, 0.45, 0.36, 0.42 in June and 0.44, 0.0, 0.12 in July, respectively. An in-depth analysis of data revealed that averaged OptDeg values, as well as their spatial variations in the horizontal profile were closer to the plants’ comfort zone in the cold season as compared with those in the warm season. This was attributed to the use of heating systems in the cold season and the lack of automated cooling devices in the warm season. This study confirmed the applicability of using IoT sensors for real-time model-based assessment of greenhouse microclimate on a commercial scale. The presented IoT sensor node and the Simulink model provide growers with a better insight into interpreting crop growth environment. The outcome of this research contributes to the improvement of closed-field cultivation of tomato by providing an integrated decision-making framework that explores microclimate variation at different growth stages in the production season.
- ItemInvestigation of the Effects of Torrefaction Temperature and Residence Time on the Fuel Quality of Corncobs in a Fixed-Bed Reactor(Basel : MDPI, 2022) Orisaleye, Joseph I.; Jekayinfa, Simeon O.; Pecenka, Ralf; Ogundare, Adebayo A.; Akinseloyin, Michael O.; Fadipe, Opeyemi L.Biomass from agriculture is a promising alternative fuel due to its carbon-neutral feature. However, raw biomass does not have properties required for its direct utilization for energy generation. Torrefaction is considered as a pretreatment method to improve the properties of biomass for energy applications. This study was aimed at investigating the effects of torrefaction temperature and residence time on some physical and chemical properties of torrefied corncobs. Therefore, a fixed-bed torrefaction reactor was developed and used in the torrefaction of corncobs. The torrefaction process parameters investigated were the torrefaction temperature (200, 240, and 280 °C) and the residence time (30, 60, and 90 min). The effects of these parameters on the mass loss, grindability, chemical composition, and calorific value of biomass were investigated. It was shown that the mass loss increased with increasing torrefaction temperature and residence time. The grinding throughput of the biomass was improved by increasing both the torrefaction temperature and the residence time. Torrefaction at higher temperatures and longer residence times had greater effects on the reduction in particle size of the milled corncobs. The calorific value was highest at a torrefaction temperature of 280 °C and a residence time of 90 min. The energy yield for all treatments ranged between 92.8 and 99.2%. The results obtained in this study could be useful in the operation and design of torrefaction reactors. They also provided insight into parameters to be investigated for optimization of the torrefaction reactor.
- ItemEnergy Systems and Applications in Agriculture(Basel : MDPI, 2022) Sultan, Muhammad; Mahmood, Muhammad Hamid; Ahamed, Md Shamim; Shamshiri, Redmond R.; Shahzad, Muhammad Wakil[No abstract available]
- ItemEvaluating Soil-Borne Causes of Biomass Variability in Grassland by Remote and Proximal Sensing(Basel : MDPI AG, 2019) Vogel, Sebastian; Gebbers, Robin; Oertel, Marcel; Kramer, EckartOn a grassland field with sandy soils in Northeast Germany (Brandenburg), vegetation indices from multi-spectral UAV-based remote sensing were used to predict grassland biomass productivity. These data were combined with soil pH value and apparent electrical conductivity (ECa) from on-the-go proximal sensing serving as indicators for soil-borne causes of grassland biomass variation. The field internal magnitude of spatial variability and hidden correlations between the variables of investigation were analyzed by means of geostatistics and boundary-line analysis to elucidate the influence of soil pH and ECa on the spatial distribution of biomass. Biomass and pH showed high spatial variability, which necessitates high resolution data acquisition of soil and plant properties. Moreover, boundary-line analysis showed grassland biomass maxima at pH values between 5.3 and 7.2 and ECa values between 3.5 and 17.5 mS m−1. After calibrating ECa to soil moisture, the ECa optimum was translated to a range of optimum soil moisture from 7% to 13%. This matches well with to the plant-available water content of the predominantly sandy soil as derived from its water retention curve. These results can be used in site-specific management decisions to improve grassland biomass productivity in low-yield regions of the field due to soil acidity or texture-related water scarcity.
- ItemEvaluation of different sensing approaches concerning to nondestructive estimation of leaf area index (LAI) for winter wheat(Auckland : Massey University, 2017) Tavakoli, H.; Mohtasebi, S.S.; Alimardani, R.; Gebbers, R.Different approaches of non-destructive estimation of the LAI in winter wheat were compared. Plant height had weak relation with the LAI, while estimated biomass showed high logarithmic relationship (R2=0.839). NDRE and REIP were logarithmically well related to the LAI (R2=0.726 and 0.779 respectively). Saturation effect of NDRE and REIP was less than NDVI. Some RGB-based indices also showed good potential to estimate the LAI. Among the indices, Gm, GMB, RMB, and NRMB were better related to the LAI. The results indicated that digital cameras can be used as an affordable and simple approach for assessment of the LAI of crops.